RP-DQN: An application of Q-Learning to Vehicle Routing Problems
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Abstract (arXiv)
In this paper we present a new approach to tackle complex routing problems with an improved state representation that utilizes the model complexity better than previous methods. We enable this by training from temporal differences. Specifically Q-Learning is employed. We show that our approach achieves state-of-the-art performance for autoregressive policies that sequentially insert nodes to construct solutions on the CVRP. Additionally, we are the first to tackle the MDVRP with machine learning methods and demonstrate that this problem type greatly benefits from our approach over other ML methods.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 25 Apr 2021.
Builds on
15 of 33 references.
References · 15
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- 2019Learning Improvement Heuristics for Solving Routing Problemscited by 164
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Citing papers per year: 2022: 1.
1 paper.